$$\text{Forecasting Greenhouse Gases in Algeria}$$

$\text{Introduction:}$

The station is located on the summit (plateau) of the second highest point of the Hoggar mountain range in the Saharan desert. The site is very remote at a distance of 50 km from Tamanrasset. Touristic activities in the area are limited due to difficult access to a few dozen visitors per day. Vegetation is extremely sparse.

$\text{Location details:}$

  • Country: Algeria
  • Latitude: 23.2625° North
  • Longitude: 5.6322° East
  • Elevation: 2710.00 masl
  • Time Zone: Local Standard Time + -1.0 hour(s) = UTC

$\text{Abstract}$

$$\text{For the past 20 years, Assekrem station located in the heights of Huggar in Southern Algeria was taking measurements of the most prominent greenhouse gases in the atmosphere as part of NOAA’s global monitoring initiative. This paper aims to develop models that best fit the trends highlighted in data using Facebook’s Prophet forecasting engine. Furthermore, we present a concise explanation of the periodic patterns in greenhouse gas emissions, their sources, sinks, and their global warming potential. Finally, we discuss the current abundance of the gases in the atmosphere and forecast their alarming levels.}$$

$\text{Introduction}$

$\text{The station is located on the summit (plateau) of the second highest point of the Hoggar mountain range in the Saharan desert. The site is very remote at a distance of 50 km from Tamanrasset. Touristic activities in the area are limited due to difficult access to a few dozen visitors per day. Vegetation is extremely sparse.}$

$\text{Data}$

$\text{The Assekrem station allows us to get a clean read with little local bias due to the remoteness of the location. This allows us to get atmospheric readings of the specific gases as opposed to local short-lived and erratic gas emissions. For example, CO2 travels and mixes in the atmosphere due to its long lifetime, allowing us to see this value as opposed to having a station right next to an industrial city, where readings will largely depend on the production for that specific factory. The high position of the station can indicate that the readings are safe from biases induced from the thermal inversion layer of the atmosphere, including breeze, wind changes, and reduced or enhanced molecular transport. This provides a sturdy ground for atmospheric readings.}$

$\text{Limitations and strengths of the data}$

$\text{The data is collected in the Assekrem station in Algeria. Located at 23.2625° North, 5.6322° East, and at a 2710 metre elevation, this is one of the most remote stations and one of the only ones in the entire Saharan region (https://www.esrl.noaa.gov/gmd/dv/site/index.php?stacode=ASK). Many different techniques are used to monitor gases on Earth, but all have some type of limitations. For this specific case, the station uses in-situ measurements, which involve a single location using high quality, wide set of instrumentation, which can take precise measurements of a small and specific geographical point. two main limitations arise from this method:}$

  • Lack of certainty for generalizations as we only look at one location
  • Gaps between measuring sties make understanding complex processes difficult $\text{In situ measurements provide what is best described as direct observations of the system. The great advantage is the way we can make use of a diverse set of instrumentation which can take the most accurate readings we can get.}$
In [1]:
from fbprophet import *
import pandas as pd
import plotly.offline as py
import plotly.graph_objs as go
In [2]:
def forecasting(gas_name, gas_code, months, unit):
    gas_data = pd.read_table('C:/Users/Taha/Desktop/algeria_ghg/'\
               +gas_code+'_ask_surface-flask_1_ccgg_month.txt',
               sep='\s{1,}', names=['stations','year','month','y'], engine='python')
    gas_data['ds'] = pd.to_datetime(gas_data[['year', 'month']].assign(DAY=1))
    
    model = Prophet(weekly_seasonality=False, daily_seasonality=False)
    model.set_auto_seasonalities
    model.add_seasonality(name='monthly', period=30, fourier_order=5)
    model.fit(gas_data)
    future = model.make_future_dataframe(periods=months,freq='M')
    forecast = model.predict(future)
    py.init_notebook_mode()
    fig1 = plot.plot_plotly(model, forecast)
    fig1.update_layout(title=str(gas_name)+' Forecast', xaxis_title='Time', 
                      yaxis_title='Parts per '+unit+'illion (PP'+unit+')')
    py.iplot(fig1)
    
    fig2 = plot.plot_components_plotly(model, forecast)
    fig2.update_layout(title=str(gas_name)+'<br>Trend & Seasonality', height=500)
    py.iplot(fig2)

$\text{Carbon Dioxide}$

In [3]:
'''
Measurements are reported in units of 
micromol/mol (10^-6 mol CO2 per mol of dry air or parts per 
million (ppm)). Measurements are directly traceable to the 
WMO X2007 CO2 mole fraction scale.
'''
forecasting('Carbon Dioxide', 'co2', 240, 'M')

$\text{Methane}$

In [4]:
'''
Measurements are reported in units of nanomol/mol 
(10^-9 mol CH4 per mol of dry air (nmol/mol) or parts per billion 
(ppb)) relative to the NOAA 2004A CH4 standard scale.
'''
forecasting('Methane', 'ch4', 240, 'B')

$\text{Carbon Monoxide}$

In [5]:
'''
Carbon monoxide mixing ratios in these files are reported 
in units of nmol/mol (10^-9 mole CO per mole of dry air 
or as part per billion by mole fraction (ppb)) relative
to the NOAA/WMO CO scale (Novelli et al., 1991, Novelli 
et al., 2003).
'''
forecasting('Carbon Monoxide', 'co', 240, 'B')

$\text{Nitrous Oxide}$

In [7]:
'''
N2O measurements are reported in units of nanomol/mol (10^-9 mol N2O 
per mol of dry air (nmol/mol) or parts per billion (ppb)) relative 
to the NOAA 2006A N2O standard scale.
'''
forecasting('Nitrous Oxide', 'n2o', 240, 'B')

$\text{Sulfur Hexafluoride}$

In [8]:
'''
SF6 measurements are reported in units of picomol/mol (10^-12 mol 
SF6 per mol of dry air (pmol/mol) or parts per trillion (ppt)) 
relative to the NOAA 2014 SF6 standard scale.
'''
forecasting('Nitrous Oxide', 'sf6', 240, 'T')

$\text{Data Source}$

  • National Oceanic and Atmospheric Administration (NOAA)
  • Earth System Research Laboratory (ESRL)
  • Global Monitoring Division (GMD)
  • Carbon Cycle Greenhouse Gases (CCGG)

$\text{Data Source}$